1. Technical Field
The present disclosure relates to systems and methods for providing automated diagnosis and decision support in medical imaging and, more particularly, to systems and methods for providing automated diagnosis and decision support in whole-body imaging.
2. Discussion of Related Art
Medical imaging is generally recognized as important for diagnosis and patient care. In recent years, medical imaging has experienced an explosive growth due to advances in imaging modalities such as x-rays, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Many existing and emerging whole-body imaging modalities are showing great potential for supporting new or improved pre-clinical and clinical applications and workflows. These modalities include whole-body MRI, positron emission tomography (PET), single-photon emission computed tomography (SPECT), PET/CT, SPECT/CT, whole-body CT (MDCT), and PET/MR.
Whole-body imaging modalities can be useful for non-organ-specific oncologic staging. For example, studies have shown that whole-body PET and whole-body MRI are more sensitive and specific than traditional skeletal scintigraphy in the assessment of metastatic bone disease. In general, whole-body PET can identify the extent and severity of disease more accurately than CT, MR or other imaging modalities. PET can be useful for detecting disease before it has grown to a detectable size for CT or MR.
Challenges in whole-body imaging include the increased data volume, anatomical or functional variability throughout the body, breathing/motion artifacts and joint articulations, and the possibly lower spatial resolution as compared to focused organ/sectional scans. For example, a whole-body scan yields a large data volume which requires more reading time. In a non-organ-specific whole-body scan, the reader such as a physician, a radiologist, or a technologist, may not know precisely what to look for or at what location, or how to differentiate normal versus abnormal, that is, physiological versus pathological uptakes in PET. In many cases, whole-body screening has less spatial resolution than a focused image study of a particular organ. Non-rigid image matching or registration is difficult for sectional/organ images such as images of the brain or the lungs, and it becomes even more difficult for whole-body scans, because of the additional articulated motion of body parts. Qualitative changes may be easier to see, but quantitative changes over time are difficult to report, particularly when accurate deformable whole-body matching is not readily achievable.
There is a vast amount of literature relating to the topics of computer aided detection, diagnosis, and decision support for medical imaging applications. Most of these are organ-specific, focusing on one of breast, brain, heart, lung, colon, etc. Image analysis applications that employ active shape models, active motion models and active appearance models generally use over-simplified statistical assumptions such as the Gaussian assumption. Existing solutions for image segmentation and registration rely on predefined procedures, such as first edge/corner detection and Hough transform, or other hand-crafted features that are not readily scalable or adaptable to changing patient sample bases, evolving disease statistics, or ever-advancing hardware technologies.